Time-Series Regeneration With Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation

For health prognostic task, ever-increasing efforts have been focused on machine learning-based methods, which are capable of yielding accurate remaining useful life (RUL) estimation for industrial equipment or components without exploring the degradation mechanism. A prerequisite ensuring the success of these methods depends on a wealth of run-to-failure data, however, run-to-failure data may be insufficient in practice. That is, conducting a substantial amount of destructive experiments not only is high costs, but also may cause catastrophic consequences. Out of this consideration, an enhanced RUL framework focusing on data self-generation is put forward for both non-cyclic and cyclic degradation patterns for the first time. It is designed to enrich data from a data-driven way, generating realistic-like time-series to enhance current RUL methods. First, high-quality data generation is ensured through the proposed convolutional recurrent generative adversarial network (CR-GAN), which adopts a two-channel This work was supported by the A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence Partnership under Grant RGANS1906 and in part supported by the National Natural Science Foundation of China under Grant 61903327. (Corresponding author: Yan Qin) X.W. Zhang and X. Liu are with the Department of Software and Microelectronics, Peking University, Peking, 100871 China. ( e-mail: zhangxuewen2018@qq.com, xliu@ss.pku.edu.cn). Y. Qin, C. Yuen, and L. Jayasinghe are with the Engineering Product Development Pillar of Singapore University of Technology and Design, 8 Somapah Road, 487372 Singapore. (e-mail: yan qin@sutd.edu.sg, yuenchau@sutd.edu.sg, lahiruaruna@gmail.com) ar X iv :2 10 1. 03 67 8v 1 [ cs .L G ] 1 1 Ja n 20 21

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